The frequency and clinical impact of synonymous HTT loss-of-interruption and duplication-of-interruption variants in a diverse HD cohort
Bibliographic record
Abstract
PURPOSE: To determine the frequency and clinical impact of loss-of-interruption (LOI) and duplication-of-interruption modifier variants of the HTT CAG and CCG repeat in a cohort of individuals with Huntington disease (HD). METHODS: We screened symptomatic HD participants from the UBC HD Biobank and 5 research sites for sequence variants. After variant identification, we examined the clinical impact and frequency in the reduced penetrance range. RESULTS: Participants with CAG-CCG LOI and CCG LOI variants have a similar magnitude of earlier onset of HD, by 12.5 years. The sequence variants exhibit ancestry-specific differences. Participants with the CAG-CCG LOI variant also have a faster progression of Total Motor Score by 1.9 units per year. Symptomatic participants with the CAG-CCG LOI variant show enrichment in the reduced penetrance range. The CAG-CCG LOI variant explains the onset of 2 symptomatic HD participants with diagnostic repeats below the pathogenetic range. CONCLUSION: Our findings have significant clinical implications for participants with the CAG-CCG LOI variant who receive inaccurate diagnoses near diagnostic cutoff ranges. Improved diagnostic testing approaches and clinical management are needed for these individuals. We present the largest and most diverse HTT CAG and CCG sequence variant cohort and emphasize their importance in clinical presentation in HD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".